Artificial Intelligence Review - Special issue on lazy learning
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
The Journal of Machine Learning Research
Towards systematic design of distance functions for data mining applications
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Formulating distance functions via the kernel trick
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A transductive framework of distance metric learning by spectral dimensionality reduction
Proceedings of the 24th international conference on Machine learning
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Relevance feature mapping for content-based image retrieval
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Context-sensitive ranking for effective image retrieval
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Relevance feature mapping for content-based multimedia information retrieval
Pattern Recognition
Capturing contextual relationship for effective media search
Multimedia Tools and Applications
Dynamic similarity kernel for visual recognition
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts
Journal of Biomedical Informatics
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Tasks of information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be formulated in a context-dependent (also application-, data-, and user-dependent) way. In this paper, we present a novel method, which learns a distance function by capturing the nonlinear relationships among contextual information provided by the application, data, or user. We show that through a process called the "kernel trick," such nonlinear relationships can be learned efficiently in a projected space. In addition to using the kernel trick, we propose two algorithms to further enhance efficiency and effectiveness of function learning. For efficiency, we propose a SMO-like solver to achieve O(N2) learning performance. For effectiveness, we propose using unsupervised learning in an innovative way to address the challenge of lack of labeled data (contextual information). Theoretically, we substantiate that our method is both sound and optimal. Empirically, we demonstrate that our method is effective and useful.